About The Position

Cirrus Venture Labs (CVL) is Cirrus Logic’s technology accelerator, focused on developing disruptive, scalable, and monetizable innovations. Their vision is to be a globally recognized innovation engine that shapes semiconductor markets by embedding intelligence directly where signals originate—across Voice, Sense, and Control domains. As an ML Research & Systems Intern, you will be hands-on, contributing to prototyping pipelines, data curation, and model optimization for deployment on constrained Edge and mixed-signal systems, turning ambitious hypotheses into validated prototypes. Cirrus Logic is a leading supplier of low-power, high-precision mixed-signal processing solutions for mobile and consumer applications, with a robust portfolio of sophisticated low-power products including boosted amplifiers, smart codecs, camera controllers, haptic driver and sensing solutions, power conversion and control ICs, and fast-charging ICs. The company leverages innovative technology, software, and associated algorithms, along with a strong intellectual property portfolio and extensive mixed-signal expertise, to drive innovation and growth in audio and high-performance mixed-signal processing technologies.

Requirements

  • Currently enrolled in a Master’s or Ph.D. program in Computer Science, Electrical Engineering, or a related field with a focus on ML/AI.
  • Strong foundational understanding of CNNs, RNNs (LSTMs), or Transformer-based architectures.
  • Hands-on experience with Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX).
  • Basic familiarity with processing time-series, audio, or sensor data.
  • Eager to work in a high-ambiguity "startup" environment within a larger corporation.
  • Ability to explain complex technical findings to a multi-disciplinary team of hardware and software engineers.

Nice To Haves

  • Experience or coursework in C/C++ or working with resource-constrained hardware (e.g., Raspberry Pi, ESP32, ARM Cortex-M).
  • Familiarity with TFLite, ONNX, or similar edge deployment toolchains.
  • Previous research experience or publications in areas like anomaly detection, reinforcement learning, or generative AI for signal processing.
  • Contributions to open-source ML projects or active participation in academic labs.

Responsibilities

  • Assist in building and testing ML models for edge intelligence, specifically focusing on audio, sensor, and control signals.
  • Support the design of model architectures and contribute to data labeling strategies, synthetic data generation, and augmentation pipelines.
  • Explore and implement model compression techniques—such as quantization, pruning, and knowledge distillation—to ensure models run efficiently on embedded systems.
  • Stay current on foundation/SLM trends and academic research; help define benchmarks and evaluation metrics to measure the success of CVL’s ML prototypes.
  • Partner with firmware, silicon, and systems engineers to understand the physical constraints of hardware and how they impact algorithmic accuracy.
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